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Abstractive text summarization has garnered increased interest as of late, in part due to the proliferation of large language models (LLMs).
The robust beauty of improper linear models in decision making
Robyn M Dawes · 1979
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When networks disagree: Ensemble methods for hybrid neural networks
Michael P Perrone and Leon N Cooper · 1995
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Automatically evaluating content selection in summarization without human models
Annie Louis and Ani Nenkova · 2009
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Estimating accuracy from unlabeled data
Emmanouil Antonios Platanios, Avrim Blum, and Tom M Mitchell · 2014
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli · 2016
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Estimating accuracy from unlabeled data: A bayesian approach
Emmanouil Antonios Platanios, Avinava Dubey, and Tom Mitchell · 2016
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Estimating accuracy from unlabeled data: A probabilistic logic approach
Emmanouil Platanios, Hoifung Poon, Tom M Mitchell, and Eric J Horvitz · 2017
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh · 2019
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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Esin Durmus, He He, and Mona Diab · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis · 2020
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Towards question-answering as an automatic metric for evaluating the content quality of a summary
Daniel Deutsch, Tania Bedrax-Weiss, and Dan Roth · 2021
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Factsumm: Factual consistency scorer for abstractive summarization
Hoon Heo · 2021
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The factual inconsistency problem in abstractive text summarization: A survey
Yichong Huang, Xiachong Feng, Xiaocheng Feng, and Bing Qin · 2021
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2023
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Metric ensembles aid in explainability: A case study with wikipedia data
Grant Forbes and R Jordan Crouser · 2023
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Survey of hallucination in natural language generation
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Smart: sentences as basic units for text evaluation
Reinald Kim Amplayo, Peter J Liu, Yao Zhao, and Shashi Narayan · 2022
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News summarization and evaluation in the era of gpt-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett · 2022
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Summac: Re-visiting nli-based models for inconsistency detection in summarization
Philippe Laban, Tobias Schnabel, Paul N Bennett, and Marti A Hearst · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
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Chatgpt as a factual inconsistency evaluator for abstractive text summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
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occams: A text summarization package
Clinton T White, Neil P Molino, Julia S Yang, and John M Conroy · 2023
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Summit: Iterative text summarization via chatgpt
Haopeng Zhang, Xiao Liu, and Jiawei Zhang · 2023
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